Visualising ganglion cell layer based on image entropy optimisation for adaptive contrast enhancement

J. H. Han, J. Cha

Research output: Contribution to journalArticle

Abstract

Optical coherence tomography cannot easily be used for visual identification of the ganglion cell layer (GCL) for diagnosing retinal diseases owing to the extremely low image contrast between adjacent layers. To solve this problem, the authors used a limit-clipping optimisation method along with the image entropy to enhance the image contrast of targeted layers. As a result, the GCL was successfully extracted using an intelligent tracking system without impacting the segmentation of other retinal layers and image morphology. The segmentation results were evaluated through comparisons with manual segmentation results provided by clinical experts. The results of this study should help realise simple and efficient discrimination of important retinal layers for the early diagnosis of glaucoma.

Original languageEnglish
Pages (from-to)25-27
Number of pages3
JournalElectronics Letters
Volume56
Issue number1
DOIs
Publication statusPublished - 2020 Jan 9

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Visualising ganglion cell layer based on image entropy optimisation for adaptive contrast enhancement'. Together they form a unique fingerprint.

  • Cite this